Department of Engineering Design and Mathematics, University of the West of England Bristol, UK.

Abstract

The urinary microbiome of healthy individuals and the way it alters with ageing have not been characterized and may influence disease processes. Conventional microbiological methods have limited scope to capture the full spectrum of urinary bacterial species. We studied the urinary microbiota from a population of healthy individuals, ranging from 26 to 90 years of age, by amplification of the 16S rRNA gene, with resulting amplicons analyzed by 454 pyrosequencing. Mid-stream urine (MSU) was collected by the "clean-catch" method. Quantitative PCR of 16S rRNA genes in urine samples, allowed relative enumeration of the bacterial loads. Analysis of the samples indicates that females had a more heterogeneous mix of bacterial genera compared to the male samples and generally had representative members of the phyla Actinobacteria and Bacteroidetes. Analysis of the data leads us to conclude that a "core" urinary microbiome could potentially exist, when samples are grouped by age with fluctuation in abundance between age groups. The study also revealed age-specific genera Jonquetella, Parvimonas, Proteiniphilum, and Saccharofermentans. In conclusion, conventional microbiological methods are inadequate to fully identify around two-thirds of the bacteria identified in this study. Whilst this proof-of-principle study has limitations due to the sample size, the discoveries evident in this sample data are strongly suggestive that a larger study on the urinary microbiome should be encouraged and that the identification of specific genera at particular ages may be relevant to pathogenesis of clinical conditions.

Heatmap showing the relative abundance of the OTUs per sample. The top panel presents the qPCR values for each sample while the bottom panel shows the percentage distribution of the OTUs' phyla for each sample.

Plot of total operons/ml per person (powers of 10—order of magnitude) for each genus that is cultivated routinely by standard microbiological testing, and those not routinely cultivated (including those not individually identified in routine culture).

A plot of the genus specific count (operons/ml) on a logarithmic base ten scale against age for routinely cultivated and not routinely cultivated bacteria (including those not individually identified in routine culture).

Graphical view of the results of the partitioning around mediods (PAMK in R) used to determine the number of clusters in the urinary microbiome datasets. In this instance two clusters were identified as being the optimal solution.